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Python Tabanlı Destek Vektörü Regresyon Modeli Kullanılarak Farklı Isıtma Hızlarında Yaşlandırılmış β Titanyum Alaşımının Sürtünme Katsayılarının Değerlendirilmesi

Yıl 2022, Cilt: 17 Sayı: 1, 239 - 246, 27.05.2022
https://doi.org/10.29233/sdufeffd.1098313

Öz

Bu çalışmada, Ti-15V-3Al-3Sn-3Cr metastabil β titanyum alaşımının mikro yapısal ve aşınma özelliklerine yaşlandırma sıcaklığına ısıtma hızının etkisi incelenmiştir. Alaşım yaşlandırma sıcaklığına 0,4°C/dk, 4°C/dk, 25°C/dk ve 50°C/dk olmak üzere dört farklı hızda ısıtılmıştır. Isıl işlemler sonrasında alaşımın mikro yapısal özellikleri Taramalı Elektron Mikroskobu (SEM) analiziyle incelenmiştir. Alaşımın mekanik özellikleri ise mikro sertlik ve aşınma testleri uygulanarak belirlenmiştir. En küçük boyda α fazları (393±43nm) 0,4°C/dk hızla yaşlandırma sıcaklığına ısıtılmış numunede elde edilmiştir. Isıtma hızının artmasıyla birlikte ise α fazları büyümüş ve böylece en yüksek mikro sertlik 0,4°C/dk hızla ısıtılan numunede, en düşük mikro sertlik ise 50°C/dk hızla ısıtılan numunede elde edilmiştir. Aşınma testleri 3N ve 10N olmak üzere iki farklı yükte ve 150 metre ve 400 metre olmak üzere iki farklı kayma mesafesinde uygulanmıştır. Aşınma testi sonrası kütle kaybı yük ve kayma mesafesinin artmasıyla birlikte tüm numune gruplarında artmıştır. Aşınma testleri sonrasında elde edilen verilere, istatistiki veri analizi uygulanarak sürtünme katsayıları tayin edilmiştir. Ayrıca aşınma testinde elde edilen sürtünme katsayısı verileri, %40 test ve %60 eğitim olmak üzere iki sete bölünmüştür. Model performansı, ortalama hata karesi, ortalama karekök sapması ve regresyon değeri dikkate alınarak değerlendirilmiştir. Model, farklı ısıtma hızlarında yaşlandırma sıcaklığına ısıtılmış numunelerin sürtünme katsayılarını %76’nın üzerinde doğrulukla tahmin edebilmiştir.

Kaynakça

  • [1] B. Gu, V. S. Sheng, Z. Wang, D. Ho, S. Osman, and S. Li, “Incremental learning for ν-Support Vector Regression,” Neural Networks, 67, 140-150, 2015.
  • [2] N. Yumak and K. Aslantas, “A review on heat treatment efficiency in metastable b titanium alloys: The role of treatment process and parameters,” J. Mater. Res. Technol., 9 (6), 15360-16280, 2020.
  • [3] L. Y. Chen, Y. W. Cui, and L. C. Zhang, “Recent development in beta titanium alloys for biomedical applications,” Metals, 10 (9), 1-29, 2020.
  • [4] N. Yumak, K. Aslantaş, and W. Ahmed, “Effect of aging treatment on the ınitiation and propagation of fatigue cracks in the Ti-15V-3Al-3Sn-3Cr metastable β titanium alloy,” in Advances in Science and Engineering Technology International Conferences (ASET), Dubai, 2020, pp. 1-5.
  • [5] N. Yumak and K. Aslantas, “Effect of heat treatment procedure on mechanical properties of Ti-15V-3Al-3Sn-3Cr metastable β titanium alloy,” J. Mater. Eng. Perform., 32(2). 1066-1074, 2021.
  • [6] P. Zhánal, P. Harcuba, J. Strasky, J. Šmilauerová, P. Beran, Hensan T., H. Seiner, and M. Janecek, “Transformation pathway upon heating of metastable β titanium alloy Ti-15Mo investigated by neutron diffraction,” Mater., 12(21), 1-16, 2019.
  • [7] J. Gao and W. M. Rainforth, “The effect of heating rate on discontinuous grain boundary alpha formation in a metastable beta titanium alloy,” Metall. Mater. Trans. A, 51 (8), 3766-3771, 2020.
  • [8] B. Song, Y. Chen, W. Xiao, L. Zhou, and C. Ma, “Formation of intermediate phases and their influences on the microstructure of high strength near-β titanium alloy,” Mater. Sci. Eng. A, 793, 1-11, 2020.
  • [9] R. N. Elshaer, K. M. Ibrahim, A. F. Barakat, and R. R. Abbas, “Effect of heat treatment processes on microstructure and mechanical behavior of TC21 titanium alloy,” Open J. Met., 7 (3), 39-57, 2017.
  • [10] S. R. Chauhan and K. Dass, “Dry sliding wear behaviour of titanium (Grade 5) alloy by using response surface methodology,” Adv. Tribol., 1-9, 2013.
  • [11] M. D. Sharma and R. Sehgal, “Dry Sliding Friction and Wear Behaviour of Titanium Alloy (Ti-6Al-4V),” Tribol. Online, 7 (2), 87–95, 2012.
  • [12] D. Parbat and M. Chakraborty, “A python based support vector regression model for prediction of COVID19 cases in India,” Chaos, Solitons & Fractals, 138, 1-5, 2020.
  • [13] A. Datta, M. J. Augustin, N. Gupta, S. R. Viswamurthy, K. M. Gaddikeri, and R. Sundaram, “Impact localization and severity estimation on composite structure using fiber bragg grating sensors by least square support vector regression,” IEEE Sens. J., 19 (12), 4463-4470, 2019.
  • [14] S. G. Setti and R. N. Rao, “Tribological behaviour of near β titanium alloy as a function of α + β solution treatment temperature, Materials & Design, 50, 997-1004, 2013.

Investigation of Friction Coefficient of β Titanium Alloy Aged At Different Heating Rates Using A Python-Based Support Vector Regression Model

Yıl 2022, Cilt: 17 Sayı: 1, 239 - 246, 27.05.2022
https://doi.org/10.29233/sdufeffd.1098313

Öz

In this study, the effect of heating rate on the microstructural and wear properties of Ti-15V-3Al-3Sn-3Cr metastable β titanium alloy was investigated. Four heating rates, namely 0.4 °C/min, 4 °C/min, 25 °C/min, and 50 °C/min, were used during heating to the aging temperature. After heat treatment, the microstructural properties of the alloy were investigated by Scanning Electron Microscope (SEM) analysis. The mechanical properties of the alloy were determined by applying microhardness and wear tests. The finest α phases (393±43nm) were observed in the sample's microstructure, whose heating rate was 0.4 °C/min.. With the increase in the heating rate, the α phases grew, and thus the highest and the lowest microhardness were observed respectively at 0.4°C/min and at 50°C/min heating rates. Wear tests were carried out at loads of 3N and 10N and with two different sliding distances, 150 meters, and 400 meters. Mass loss after the wear test increased in all sample groups with increasing load and sliding distance. The friction coefficients were determined by applying statistical analysis to the data obtained from the wear tests. The data was divided into two sets, such as 40% test and 60% training. Model performance was evaluated by considering the mean square error, root means square error, and regression score value. The model was able to predict the friction coefficients of the samples heated to the aging temperature at different heating rates with an accuracy of above 76%.

Kaynakça

  • [1] B. Gu, V. S. Sheng, Z. Wang, D. Ho, S. Osman, and S. Li, “Incremental learning for ν-Support Vector Regression,” Neural Networks, 67, 140-150, 2015.
  • [2] N. Yumak and K. Aslantas, “A review on heat treatment efficiency in metastable b titanium alloys: The role of treatment process and parameters,” J. Mater. Res. Technol., 9 (6), 15360-16280, 2020.
  • [3] L. Y. Chen, Y. W. Cui, and L. C. Zhang, “Recent development in beta titanium alloys for biomedical applications,” Metals, 10 (9), 1-29, 2020.
  • [4] N. Yumak, K. Aslantaş, and W. Ahmed, “Effect of aging treatment on the ınitiation and propagation of fatigue cracks in the Ti-15V-3Al-3Sn-3Cr metastable β titanium alloy,” in Advances in Science and Engineering Technology International Conferences (ASET), Dubai, 2020, pp. 1-5.
  • [5] N. Yumak and K. Aslantas, “Effect of heat treatment procedure on mechanical properties of Ti-15V-3Al-3Sn-3Cr metastable β titanium alloy,” J. Mater. Eng. Perform., 32(2). 1066-1074, 2021.
  • [6] P. Zhánal, P. Harcuba, J. Strasky, J. Šmilauerová, P. Beran, Hensan T., H. Seiner, and M. Janecek, “Transformation pathway upon heating of metastable β titanium alloy Ti-15Mo investigated by neutron diffraction,” Mater., 12(21), 1-16, 2019.
  • [7] J. Gao and W. M. Rainforth, “The effect of heating rate on discontinuous grain boundary alpha formation in a metastable beta titanium alloy,” Metall. Mater. Trans. A, 51 (8), 3766-3771, 2020.
  • [8] B. Song, Y. Chen, W. Xiao, L. Zhou, and C. Ma, “Formation of intermediate phases and their influences on the microstructure of high strength near-β titanium alloy,” Mater. Sci. Eng. A, 793, 1-11, 2020.
  • [9] R. N. Elshaer, K. M. Ibrahim, A. F. Barakat, and R. R. Abbas, “Effect of heat treatment processes on microstructure and mechanical behavior of TC21 titanium alloy,” Open J. Met., 7 (3), 39-57, 2017.
  • [10] S. R. Chauhan and K. Dass, “Dry sliding wear behaviour of titanium (Grade 5) alloy by using response surface methodology,” Adv. Tribol., 1-9, 2013.
  • [11] M. D. Sharma and R. Sehgal, “Dry Sliding Friction and Wear Behaviour of Titanium Alloy (Ti-6Al-4V),” Tribol. Online, 7 (2), 87–95, 2012.
  • [12] D. Parbat and M. Chakraborty, “A python based support vector regression model for prediction of COVID19 cases in India,” Chaos, Solitons & Fractals, 138, 1-5, 2020.
  • [13] A. Datta, M. J. Augustin, N. Gupta, S. R. Viswamurthy, K. M. Gaddikeri, and R. Sundaram, “Impact localization and severity estimation on composite structure using fiber bragg grating sensors by least square support vector regression,” IEEE Sens. J., 19 (12), 4463-4470, 2019.
  • [14] S. G. Setti and R. N. Rao, “Tribological behaviour of near β titanium alloy as a function of α + β solution treatment temperature, Materials & Design, 50, 997-1004, 2013.
Toplam 14 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Metroloji,Uygulamalı ve Endüstriyel Fizik
Bölüm Makaleler
Yazarlar

Nihal Yumak 0000-0003-4492-7142

Yayımlanma Tarihi 27 Mayıs 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 17 Sayı: 1

Kaynak Göster

IEEE N. Yumak, “Python Tabanlı Destek Vektörü Regresyon Modeli Kullanılarak Farklı Isıtma Hızlarında Yaşlandırılmış β Titanyum Alaşımının Sürtünme Katsayılarının Değerlendirilmesi”, Süleyman Demirel University Faculty of Arts and Science Journal of Science, c. 17, sy. 1, ss. 239–246, 2022, doi: 10.29233/sdufeffd.1098313.